Automatic face recognition in unconstrained conditions is subject to pose, expression, and illumination variability. Automatic face recognition is, perhaps, among the most challenging machine vision tasks and has far-reaching applications—ranging from entertainment to security. Broadly, face recognition tasks can be categorized as one-to-one verification (e.g., a biometric automated teller machine authenticating the claimed identity of a cardholder), and one- or few-to-many identification (e.g., finding one or a few black-listed persons in a large crowd).
While the past few decades of research showed a steady improvement in face recognition accuracy, achievements of the last few years have been more rapid. On one-to-one face verification tasks, modern learning techniques achieve performance only a notch inferior to that of humans. These approaches generate high-dimensional features decreasing the intra-subject variance due to pose, expression, illumination, and other factors, while increasing the inter-subject variance.
More modest achievements have been made in the one- and few-to-many category.